Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

Abstract In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos...

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Bibliographic Details
Main Authors: Hyuk Jin Kim, Minsu Chong, Tae Gyu Rhee, Yeong Gwang Khim, Min-Hyoung Jung, Young-Min Kim, Hu Young Jeong, Byoung Ki Choi, Young Jun Chang
Format: Article
Language:English
Published: SpringerOpen 2023-02-01
Series:Nano Convergence
Subjects:
Machine learning
RHEED
Principal component analysis
K-means clustering
TMDC
ReSe2
Online Access:https://doi.org/10.1186/s40580-023-00359-5

Internet

https://doi.org/10.1186/s40580-023-00359-5

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